COVID Analysis

Check out my COVID Analysis

This is the approach I used. I got the data from this source: ECDC

Import Data

This is where we import data.

#these libraries need to be loaded
library(utils)

#read the Dataset sheet into “R”. The dataset will be called "data".
data <- read.csv("https://opendata.ecdc.europa.eu/covid19/nationalcasedeath_eueea_daily_ei/csv", na.strings = "", fileEncoding = "UTF-8-BOM")

Schedule R Scripts

library(taskscheduleR)

taskscheduler_create(taskname = "test_run", rscript = "C:/Users/jzqma/Documents/GithubRepos/Basic-COVID19-ETL-Pipeline-R/covid_19-notebook.Rmd", 
                     schedule = "ONCE", starttime = format(Sys.time() + 25, "%H:%M"))

[1] "SUCCESS: The scheduled task \"test_run\" has successfully been created."

#taskcheduler_stop("test_run")
#taskscheduler_delete("test_run")

Show Data Table

library(dplyr)

Attaching package: 㤼㸱dplyr㤼㸲

The following objects are masked from 㤼㸱package:stats㤼㸲:

    filter, lag

The following objects are masked from 㤼㸱package:base㤼㸲:

    intersect, setdiff, setequal, union

library(DT)

Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

edf <- data %>% group_by(countriesAndTerritories) %>% 
  mutate(deathRate = sum(deaths) / sum(cases))

datatable(edf, extensions = 'Buttons', options = list(
  dom = 'Bfrtip',
  buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
))

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Graph 1

Graph A

library(ggplot2)

edf %>% filter(deaths > 100) %>% 
  group_by(countriesAndTerritories) %>% 
  summarise(Deaths_Total = sum(deaths)) %>% 
  ggplot(aes(x = countriesAndTerritories)) + 
  geom_point(aes(y = Deaths_Total)) +
  coord_flip()

Graph B

edf %>% filter(deaths > 100) %>% 
  group_by(countriesAndTerritories) %>% 
  summarise(Cases_Total = sum(cases)) %>% 
  ggplot(aes(x = countriesAndTerritories)) + 
  geom_point(aes(y = Cases_Total)) +
  coord_flip()

Input rmarkdown::render('covid_19-notebook.Rmd') in console to view HTML RMD